Neural Network Wavefront Reconstruction via Zernike Segmentation
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Solution Overview
Problem
Existing methods for analyzing turbulent wavefronts in laser beams, such as those used in ophthalmology and navigation, are time-consuming and often cannot provide real-time corrections, making them impractical for complex applications.
Innovation Solution
A neural network-based method that disassembles the scanning function into phase information components, compares them with a set of comparison functions to select a dominant representation function, and uses this to mathematically reconstruct the turbulent wavefront, employing a two-dimensional receiver arrangement with optical sensors and a memory of comparison functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze turbulent wavefronts and determine phase corrections, then measurement precision can be achieved, but processing time becomes too long for real-time applications
Solution Approach 1:
The patent segments the turbulent wavefront analysis into discrete Zernike polynomial modes, where each mode represents a specific phase aberration component. The neural network processes each Zernike coefficient independently through parallel computation, allowing the complex wavefront reconstruction to be divided into manageable, simultaneously executable tasks that maintain precision while reducing overall processing time
Solution Approach 2:
The patent replaces traditional mechanical/optical sequential processing methods with a neural network-based computational system. The neural network performs parallel mathematical operations to compute Zernike coefficients and reconstruct the wavefront, substituting the sequential mechanical measurement and calculation process with a faster electronic computational approach that achieves real-time performance
2Productivity
If a neural network is used to process phase information in parallel, then processing speed increases for real-time applications, but device complexity increases
Solution Approach 1:
The patent transforms the wavefront representation parameters into Zernike polynomial coefficients, which serve as standardized input parameters for the neural network. This parameter transformation simplifies the neural network architecture by providing a well-defined mathematical basis, reducing the complexity of the network design while enabling parallel processing of phase information at high speed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, nearly real-time phase corrections in complex cases by processing phase information in parallel, improving the efficiency and accuracy of wavefront analysis.
Implementation Method 1
a preferably at least two-dimensional arrangement of optical elements such as lenses, diffraction gratings or the like., To convert the incident sampling in phase information components contained can
Data Source
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AI summary
The method involves transmitting a sampling function to a neuronal network for processing, and providing comparison functions to the network. A primary comparison function is selected by comparing the sampling function with the comparison functions, where a preset variation or an extreme variation value serves as criteria for the selection of primary comparison function. The selected primary comparison function is determined as a dominant representation function that is removed by the sampling function in case of the preset variation or extreme variation value is determined. An independent claim is also included for a device for implementing a sampling function by a neuronal network.